[biomodel]

Always wonder who these kinds of reviews / surveys are for? Nobody is going to learn machine learning by reading a 50 page pdf. Meanwhile, people that have experience will have a hard time finding the info they don't already know.

Opinionated & narrow >> Shallow & comprehensive

[throwawayjava]

A good review article is worth its weight in gold for both the researchers who write it and the research community.

Remember that research communities are extremely transient because of the professor : phd student : practitioner ratio and the low odds that a graduated phd student a) stays in research and then b) stays in the same research area for their whole career. Therefore, most members of a given research community have approximately 1-3 years of experience in the broader academic field and approximately no experience in the area covered by the review. Therefore, a good review can simultaneously:

1. prevent a lot of wheel re-invention, and

2. push the research field in a certain direction (either accidentally or purposefully).

Also, good review articles typically include some amount of synthesis. I.e., the creation of a conceptual framework and language for understanding and talking about a bunch of vaguely related stuff. This article tries to do that e.g. in Section 2.1 but the topic of the review is so incredibly broad that the categories are not super useful.

[abdullahkhalids]

They are useful for someone in a nearby field trying to learn this field. That person first reads the textbook, and a few specific papers. Then once, they have a good narrow understanding, they broaden it by reading one of these review papers.

In essence, a review paper saves you the trouble of doing a literature review in a new subfield, because it identifies the important papers for you.

That said, the reason review papers are usually written is for the authors to cement their own understanding of the network of research in the field.

[mistrial9]

I will read it, to defend my non-DeepLearning choices for supervised ML .. so many on the bandwagon for unsupervised CNN with their GPUs

[mistrial9]

I am misunderstood here.. it means, for the purposes that are appropriate, use a disciplined, supervised model.. and know the strengths and weakness' of the CNN models.. yes, some reaction to the hype of CNN..